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Facebook got a lot of criticism over the Cambridge Analytica breach, and Zuckerberg vowed to do better in protecting users’ privacy in a full-page ad. But this is not the first time political campaigns have used social media user data during elections — the only difference was that millions of users did not even know the platform was harvesting their data and using it to target them for political purposes.

The bigger problem is that what happened to Facebook was inevitable. Sure, Facebook as a closed system is especially harmful. A system that can see your current interactions, has control over the content it shows you, and can measure the results of those things is a perfect fit for human behavior optimization.

What I’m saying is that even if we did not have the Cambridge Analytica scandal, the fact would remain that social channels are harvesting our data. Take Twitter, for instance. You can easily see any likes and interactions people have had — that data is open to everyone. Use the Twitter API and you can automate its collection. Connect it to IBM Watson or some other enterprise service and you will instantly get access to thousands (if not millions) of records. And this data is not private by any means.

The ingenious idea is to build a psychological profile based on the “likes” of users, then learn who to target and how to target them. Once you have built this profile, you can use it any way you please.

The cycle does not need to be rooted in Facebook — one could build a profile from Twitter data and use that in Facebook ads. You only need the profile to train the AI, and once you’ve trained it, the technology can work its magic on any platform.

AI is getting more aggressive

As AI grows more intelligent, it will be able to read and analyze data from disparate sources. It will not need a feed of uniform data or dozens of operators to scan and extract the signal from the noise. For instance, there are AI technologies that can scan thousands of records in a matter of minutes and return results. This means that AI can scan websites, files, and documents and form a complete profile for us without breaking a single privacy law.

The information is out there, free for the public — it only becomes gold when a machine learning engine traverses all of them, collects the data in a single place, creates a profile based on it, and fills the gaps accordingly — all within minutes.

Many users felt manipulated by Facebook following the Cambridge Analytica scandal. This has led us to start questioning the ways the company acquired the data they used. However, soon companies like CA will have that data anyway, even without Facebook. We cannot even be sure that right this moment, the same thing is not happening again. Moreover, as I described above, companies can collect this information via completely legal means.

The problem is not Facebook. The problem is that we are not prepared for the threats that surround us.

The real threat

AI is most feared for its potential to either replace humans at work or annihilate them altogether. However, AI can’t really get creative — it can only repeat what humans do, though sometimes more efficiently. While it surely does a better job than many people in certain fields, leading to replacement worries, AI also creates new opportunities. Besides, automation attempts at major companies such as Tesla have proved that overdoing AI optimization is not practical — at least not yet.

The threat of AI taking our jobs or attacking humans isn’t as imminent as the threat of humans using the technology for nefarious purposes. It’s how we use AI that causes the real threat. For example, companies like Netflix and Facebook can use our psychological profiles to help us find new friends with similar interests or offer tailored recommendations for TV shows without issue. However, in the case of Cambridge Analytica, the company used these profiles to elicit a certain behavior from the targets without their knowledge, which is setting off alarms for good reason.

A more severe possibility for the technology involves companies using your content and connections to shift your ideas. For instance, if you publish content that contains ideas that the system wants to dissuade you from, it could share it only with people with opposite views, creating tons of negative reviews and the impression that nobody agrees with you. Likewise, if your piece contains issues the system wants you to hold onto or strengthen, it can share it only with like-minded people so you only receive positive feedback.

Taking this a step further, governments could potentially use this technology against their people. For instance, China’s censorship effectively creates a closed system that is totally vulnerable to these kinds of manipulations. Even security agencies like those revealed by Edward Snowden could control your traffic at the router level.

How to protect ourselves

AI will not go away. Our information is out there, and we cannot solely rely on regulations to protect us. Savvy individuals outpace regulations by constantly creating new ways to alter our behavior. You might take the blockchain route to conceal and stamp everything, but since not everyone is 100 percent on the blockchain, there will still be data leaks. This is why I believe in Alan Turing’s approach that only a machine can defeat another machine; thus, we need to arm and catch up with our own AI tools.

An AI assistant that protects the interests of its user could be a feasible solution. This AI would need to be transparent and decentralized so we could be certain it wouldn’t serve any other parties behind the scenes. Such AI could “break the loop.” For instance, it could detect patterns of behavior optimization and understand what a publication is trying to make you do, and warn against that. The technology could even alter the content or block parts of it to neutralize such attempts. In the case of channeled traffic, an AI assistant could be helpful by detecting such patterns and automatically sharing the content beyond a single platform, all while sending the results back to the user.

Much of what we thought about AI hasn’t happened, and a lot of things we did not think would happen have. In the end, what we are really up against is the humans behind the machines, rather than the machines themselves.

David Petersson is a developer and tech writer who contributes to Hacker Noon.

I remember feeling that way, Mr. Badger, but they’re simple… if you can SEE them!

I tried solving this problem and the internet led me to SUMMARIZE(). It was stuffed inside another function and it confused me. I felt a lot like my friend in the above picture… discouraged. DAX had been so fluid until this point! There were learning curves and lots of new ideas, but I had never bumped into a function and heard my brain just say, “Nope.”

The problem was that with all of the others I had been exposed to, the functions were “follow-able.” Either the function intuitively followed its name or I was able to go into my data model and filter some things and SEE what was really happening (by the way, Power BI, get on that). With SUMMARIZE(), there were things happening and I couldn’t see under the hood.

Before Power BI calculated tables, the only way I knew of to see the results of a DAX table function was DAX Studio (made by our friends over at SQLBI). With this calculated table functionality in Power BI, seeing SUMMARIZE() is as easy as writing it. I’ve built a simple AdventureWorks data model with a Calendar, list of Products, and Sales.

Let’s start with a simple example: Summarizing one table…

We can go to Modeling>New Table in Power BI and try out SUMMARIZE() and see how it transforms a table:

SUMMARIZE() works by taking the table we gave it in the first argument (Calendar) and grouping the rows by the distinct combinations found in the columns we gave it in the second and third arguments (Calendar[CalendarYear] & Calendar[CalendarQuarter]). I like to think of it as “VALUES()-Plus.”

To bring this full circle, let’s stuff our SUMMARIZE() into a formula and show how much SEEING the result helps us to understand what’s going on… Let’s say we want to know our average quarterly sales…

In the first measure, I simply write a measure to total up our sales dollars. In the second, we iterate over the combinations of ‘Calendar'[CalendarYear] and ‘Calendar'[CalendarQuarter] and treat each of them as if they were coordinates on a pivot table (filter context). This produces a SalesTotal for each Year/Quarter combo that we then take the average of.

Let’s say my explanation in the previous paragraph left you feeling a bit lackluster and you want to SEE the [SalesTotal] for each combination of Year and Quarter in a table. That’s where ADDCOLUMNS() comes in! Let’s take our Summarize_Test table from above, throw in an ADDCOLUMNS() and take a peek:

AddCol_Test =

ADDCOLUMNS(

SUMMARIZE(

‘Calendar’,

‘Calendar'[CalendarYear],

‘Calendar'[CalendarQuarter]

),

”Sales”,[SalesTotal]

)

ADDCOLUMNS() works very much the same as my explanation of [AvgQtrlySales] above, without any averaging; it took each combination of Year and Quarter as if they were coordinates on a pivot table (filter context) and it calculated [SalesTotal]. The first argument is the name of a table or the result of a table function (like SUMMARIZE()), the second is the name of the column we are about to add, and the third is the rule for creating it.

A nagging question you may have had… “I can get these answers using a simple pivot table, why learn this?” The answer here is simple: Sure, we can get the values that ended up in our tables, but what if I want to use them dynamically in other calculations? Even if I use DAX to generate these tables, they will only refresh if I change the formula or refresh the data model. This provides a path for dynamic table calculation!

If we’re trying to hone in on what SUMMARIZE() and ADDCOLUMS() really do, SUMMARIZE() is the grouping guru and ADDCOLUMNS() is best at adding columns to DAX tables! The power that both of these functions provide far surpasses what we’ve talked about in our intro examples. Just like the first time I used CALCULATE(), we can use these patterns, without fully understanding them, to make it rain money for our companies. Play around with them and you’ll find all sorts of nuanced behavior, additional capabilities, and performance hacks.

#Badger

We get it: you probably arrived here via Google, and now that you’ve got what you needed, you’re leaving. And we’re TOTALLY cool with that – we love what we do more than enough to keep writing free content. And besides, what’s good for the community (and the soul) is good for the brand.

But before you leave, we DO want you to know that instead of struggling your way through a project on your own, you can hire us to accelerate you into the future. Like, 88 mph in a flux-capacitor-equipped DeLorean – THAT kind of acceleration. C’mon McFly. Where you’re going, you won’t need roads.

General Artificial Intelligence is likely possible, but it’s unlikely we’ll create it from the methods we’re now utilizing. It’s not that we can’t use the current blueprint to build something strong enough to greatly improve life—or end it—but it won’t be human-like but rather something that’s at best parallel to humanness. We’ll learn about this pseudo-superintelligence by trial and error for the foreseeable future, which is always perilous when we’re talking about powerful tools that develop gradually—and then all at once.

Terry Winograd, an AI pioneer who had second thoughts, tells Aaron Timms in an OutlineQ&A that correcting the mistakes that develop along the way to more and more profound machine intelligence usually will require a large-scale failure that will elicit a course correction. “You have to wait for breakdowns,” he says, using Facebook’s great election-year failure as an example. An excerpt:

Question:

How close do you think we are to achieving “general AI”?

Terry Winograd:

I’m still in the agnostic phase — I’m not sure the techniques we have are going to get to general AI, person-like AI. I believe that nothing’s going on in my head that isn’t physical — so in principle if you could reproduce that physical structure, you could build an AI that’s just like a person. Today’s techniques are not close to that in a direct sense. Everybody knows that my brain does not operate by having trillions of examples. The mechanisms that work for AI practically today aren’t mirrors of what goes on in the brain.

Question:

How do you judge this moment in the public debate about AI? Is all this fear-mongering a useful contribution? Is it fair? Is it silly?

Terry Winograd:

Having those questions out for discussion is good, getting large amounts of hysteria and publicity isn’t. The question is: How do you raise these issues in a thoughtful way without saying, “Skynet is upon us”? Musk, I think, is more on the “clickbait” end of the public discussion about AI. But I do believe that AI is facilitating huge problems for our society — not because it’s going to be smart like a person but because robotics is going to change the whole employment picture, and because the use of AI in decision making is going to move decision-making toward directions that may not have the element of human consideration.•

We recently commissioned a study from independent research company Ovum on how organizations are tackling cybersecurity and what they plan to do next. Losses because of a data breach or other cyberattack can be severe, particularly when factors such as customer and shareholder confidence are taken into account. We therefore expected that cyber risk insurance would be an increasingly important way in which organizations are mitigating their risk.

The results were far from uniform:

The UK was the most insured country we surveyed, with 69% of respondents holding some kind of insurance, and the USA was the least insured – only 51% of US respondents had any kind of cyber risk insurance.

Across the industries surveyed, financial services firms were the most likely to be insured (71%), and healthcare the least likely (26 percent).

Even when businesses have invested in cyber risk insurance, it’s unlikely to cover them for all likely risks.

We dug a little deeper into the attitudes of our respondents to try to uncover why under insurance might occur. Three explanation emerged – each is playing a part:

They have limited investment in cybersecurity. 60% of those interviewed have seen an increase in attacks in the past year and 62% expect the overall level of threat from cyber-attacks and data breaches to increase in the coming year. Many respondents are also facing more consequences should they lose customer data, with legislation such as General Data Protection Regulation (GDPR) massively increases the fines that can be imposed. Even so, less than half (48%) expect spending on cybersecurity to increase in the coming year. While it is encouraging to see 23% are looking to invest in cyber-risk insurance, the pressure on finances may mean that they actually can’t afford to do this – or they can only take out insurance to cover the most obvious threats.

They think it won’t happen to them. We asked respondents how cyber-ready they thought their business was compared to their competitors. 60% think they are above average or top performers, while only 6% think they are below average – this is statistically unlikely. With an unrealistic view on how well they are doing, it’s probable that they don’t appreciate their true risk and therefore don’t see the need for comprehensive insurance cover. It seems that many don’t have the ability to make objective judgements about their cybersecurity risk. This becomes evident when we look at how they benchmark their cybersecurity status; 38% use their own benchmarks and criteria and 6% don’t carry out measurable assessments.

They are unclear on how premiums are set. Businesses that invest in cybersecurity want to understand what they are paying for and the value it delivers. For cyber risk insurance, this means not only understanding what the policy covers but also having confidence that the premiums charged accurately reflect risk. Only 23% believe that pricing from insurance companies is clear and transparent. 23% believe the insurance assessment for their business isn’t accurate, 19% say their premiums are based just on industry averages and 5% don’t understand how their business is assessed for cyber risk insurance.

Risk Measure Is Key to Cybersecurity Insurance

Ultimately, the part cyber risk insurance can play is dependent on a measurement of risk that both the insurer and insured can agree on. In this way businesses, are less likely to over-estimate their cyber-readiness and can build a trusted relationship with insurers based on a common understanding of the cover they need.

We have developed the FICO Enterprise Security Score to help businesses objectively assess their own cybersecurity status, as well as that of third parties. FICO Enterprise Security Score accesses billions of external data points at internet scale, and compares the subject’s cybersecurity posture to the pre-breach status of known attacks. Applying our analytics to this data gives an empirically derived score, so that:

Businesses have an objective measure of their cybersecurity status.

Insurers can score organizations to determine risk and set fair and competitive premiums.

Insurers can understand the risk across their customer portfolio.

The transparency offered by a score like this can help businesses make a more well-informed decision about whether to take out cyber risk insurance — and make sure they’re getting the best deal.

Entering into any relationship – making that big or not so big commitment – can be fraught with anxiety whether this is the right choice. No one wants to be in a relationship that isn’t working. And the same is true when evaluating your marketing automation platform.

You could be wasting valuable time with the wrong tech partner. But how do you know when it’s time to pull the plug? Like many of us, it’s hard when you’re in the relationship to see the signs and red flags. Often, we benefit from an outside perspective. Let’s look at some clues that your relationship with your marketing automation (MA) platform might not be working out.

1. Too High-Maintenance

It’s 10 a.m. and a last minute email needs to go out in the next 30 minutes, but your high- maintenance MA system mockingly stares back at you: offering limited email templates, requiring HTML expertise to customize. Plus, it doesn’t maintain a dynamic email list, which means you’ll need to go in and manually update it yourself.

Don’t let a high maintenance system hold you back. Here are a few ways marketing automation tools should make your marketing life easier:

Building your emails and landing pages should be simple and not require help from IT or outside consultants

Templates should have responsive design built in. Your MA tool should make viewing content on all sizes of devices foolproof for you

Easily design your own custom templates – the way you want – and not be limited to just the stock ones provided

And when it comes to the complicated stuff, like creating nurturing programs, your platform should be easy like Sunday Morning – such as the ability to build workflows with a drop-and-drag tool.

If you are feeling saddled with a very high-maintenance marketing automation software platform; if you miss simplicity and want to have the freedom to choose your own path, it might be a sign that it’s just not working out.

2. Language Barriers

Unless you have the technical language proficiency to read and/or write HTML, JavaScript and/or CSS (and more), you’d better not pick a marketing automation platform that requires programming knowledge. That is, of course, unless you have time dedicated to learning those languages or have a team of IT techies at your service to serve as your interpreter.

If the language barrier is already an issue and you’re about to experience a literal “communication breakdown,”it might be a sign that it’s just not working out.

3. The Frustration Factor

The clock is ticking, and in the middle of setting up your email something goes terribly wrong. You submit an online support ticket, wait patiently for what seems to be days, and never hear back. You then decide to call tech support for help and when they finally pick up, they take down your information, and then explain that a technician will call you back when they become available (usually when you are away from your desk). You can’t save the screen you are working on due to faulty HTML programming and you’ll lose your work if you exit. It worked before and you only changed a few words! ARRRG…why is this so difficult to use?

If you feel like you’re always playing a frustrating waiting game because you need significant tech support, it might be a sign that it’s just not working out.

4. Money: Hidden Surprises

We’re talking about those unforeseen, but actually-seeable-if-you-look-really-closely things that can be avoided. These little surprises might show up as financial charges for things you thought were free, or built into the price. Or your total cost of ownership might be a lot higher than you expected once you add in the cost of consultants to help in system design, architecture and maintenance.

Also: Pay attention to your database. As a growing company, you should be excited about new contacts, not dreading the new cost associated with storing them. Are you charged for how many people you have in your database or only for your active contacts? Are you warned if you are about to go over your threshold or are you just charged? Surprise!

If you are getting too many ongoing unpleasant surprises – it might be a sign that it’s just not working out.

44% of marketers are not fully satisfied with their marketing automation systems, the top 3 reasons being that the software takes too long to implement, is difficult to learn and is too expensive. – Autopilot, 2015

5. Everyone Needs Support (He’s just not that into you.)

Last but not least, do you feel supported? In a healthy and positive relationship, you should feel nurtured and supported:

You don’t want to have a vendor who comes only when called to fix something broken. Instead, you should have a vendor who proactively notices and reaches out when things are looking off.

Did your vendor’s customer support help you get thoroughly trained on the product’s capabilities?

Did they ensure everything functioned correctly from the get-go, setting you up for quick success?

Does your vendor really understand your organization’s needs and goals? Are they committed to helping you be successful and see ROI on your MA investment?

Do they stay in touch and ensure you are using all the features of your marketing automation platform?

If your customer support makes you feel like you’re skydiving without a parachute, it’s probably a sign that it’s just not working out.

Act-On’s onboarding process and their university taught more about marketing and marketing automation than I ever could have imagined. – Kevin Rice, Technology Coordinator, Quantum Learning Network

Moral of the story

It’s simple: If your current marketing automation platform is causing you or your marketing team stress in any of these ways (or ways we didn’t mention) it might be time to start looking at other solutions. And, yes, there are other solutions that are right for you.

Don’t settle. You deserve better. Want to learn more about how marketing automation vendors stack up when it comes to customer service and ease of use? Read the latest reviews from G2 Crowd.

In the sage words of Marc Andreessen, “Data is eating the world.” Er, something like that. Big data has been and continues to be one of the hottest areas in tech, constantly covered in the news. According to CB Insights, it’s also an exciting area for venture funding, with almost $ 8 billion in funding last year, a number that decreased recently but had been steadily growing since 2011 (around 20% year over year). Yet despite the popularity, most companies seem to agree on one, major problem: There aren’t enough people who want to work with data for a living.

The statistics (perhaps ironically) are pretty convincing. Summarized in an article at Datanami, McKinsey says that by 2018, the demand for data scientists will outpace supply by 60%. Accenture noted that 90% of its clients were looking for data talent, and 40% cited a lack of it as a major problem. And to top it off, Glassdoor found that the median starting salary for a data scientist can be almost double that of a programmer. Everybody’s looking to hire and pay (well) for data people, but they can’t seem to find them.

I’m a data science student at NYU’s Business School, so I see and talk to fellow students all the time about the careers they’re interested in, and unsurprisingly, data science is never at the top. Along those lines, and considering what drove me to choose data science as my major, I think that there are two key issues holding students back from being interested. If you’re a business looking to poach data talent straight out of college, you should read this.

Big data can be a societal and economic good

College students think big – they’re passionate about and interested in big ideas that have the potential to change the world and make people’s lives better. It’s why we’re so involved in and excited about politics. Programming is exciting – companies like Google and Facebook (=programmers) are doing amazing things, like Google’s Loon and Facebook’s Internet. But data sounds boring, doesn’t it? Facebook talks about how its engineers are programming and creating the products of tomorrow; its data science careers page is … less exciting.

Fortunately, big data does have the potential to change the world and make people’s lives better. For a few ideas, check out Harvard’s page on the topic. An example is using data science to increase crop yields, and eventually prevent food shortages. Big data is powerful and can be part of the big, world-shaking ideas that college students want. If you’re a company looking to generate interest in data science at schools, emphasize what potential societal and social impacts students can have as data scientists at your company.

Big data is intuitive and clear

Data science, at least on the surface, can be pretty cloudy, and most people (and students) don’t even know what it means to be a data scientist. This was once a challenge for software engineering – but companies like Codecademy and CodeMonkey, along with initiatives like Scratch, have been part of a movement that democratized coding and made it simpler and more accessible, at least at the beginner level. An easy code editor in Codecademy has shown students (plenty of my friends use it) what coding is, how you generally do it, and why it’s exciting. Bottom line: if you’re a college student, chances are you know what programming is, and you might have even tried it a little bit.

That’s not true for data science. Students generally have heard of the term but don’t know what a data scientist does, or the tools he/she would use at the job. The bulk of basic data science work – numpy, matplotlib, and pandas, if you’re using Python – isn’t easy, but it’s often intuitive and powerful. Students need a clear walkthrough and framework for what data science is, how they do it, and what cool things they can find and create with it. Something interactive like Codecademy for a relevant data set, like world population or GDP, could help get students interested.

I think that these are the core issues, but they aren’t the only solutions offered. Amy Gershkoff believes that more formal education and frameworks are needed. But I’m taking a different route – I think it’s more about the big picture potential and practical hands-on experience. These two topics – the potential positive impact of data science and the cloudiness surrounding it – definitely overlap. But solving them could be the breakthrough needed to get students interested and majoring in the field. Let’s get to it.

Justin Gage is a student at NYU’s Stern School of Business, where he’s majoring in Data Science. He’s the National Director of Consulting for TAMID Group, a student organization on 30+ college campuses that develops student business skills through hands-on interaction with the Israeli economy. This summer, he’s working at Cornerstone Venture Partners, an early stage VC firm that invests in software companies, with sights on VC for the long run. He loves fashion, well designed stuff, and all things tech.

In the sage words of Marc Andreessen, “Data is eating the world.” Er, something like that. Big data has been and continues to be one of the hottest areas in tech, constantly covered in the news. According to CB Insights, it’s also an exciting area for venture funding, with almost $ 8 billion in funding last year, a number that decreased recently but had been steadily growing since 2011 (around 20% year over year). Yet despite the popularity, most companies seem to agree on one, major problem: There aren’t enough people who want to work with data for a living.

The statistics (perhaps ironically) are pretty convincing. Summarized in an article at Datanami, McKinsey says that by 2018, the demand for data scientists will outpace supply by 60%. Accenture noted that 90% of its clients were looking for data talent, and 40% cited a lack of it as a major problem. And to top it off, Glassdoor found that the median starting salary for a data scientist can be almost double that of a programmer. Everybody’s looking to hire and pay (well) for data people, but they can’t seem to find them.

I’m a data science student at NYU’s Business School, so I see and talk to fellow students all the time about the careers they’re interested in, and unsurprisingly, data science is never at the top. Along those lines, and considering what drove me to choose data science as my major, I think that there are two key issues holding students back from being interested. If you’re a business looking to poach data talent straight out of college, you should read this.

Big data can be a societal and economic good

College students think big – they’re passionate about and interested in big ideas that have the potential to change the world and make people’s lives better. It’s why we’re so involved in and excited about politics. Programming is exciting – companies like Google and Facebook (=programmers) are doing amazing things, like Google’s Loon and Facebook’s Internet. But data sounds boring, doesn’t it? Facebook talks about how its engineers are programming and creating the products of tomorrow; its data science careers page is … less exciting.

Fortunately, big data does have the potential to change the world and make people’s lives better. For a few ideas, check out Harvard’s page on the topic. An example is using data science to increase crop yields, and eventually prevent food shortages. Big data is powerful and can be part of the big, world-shaking ideas that college students want. If you’re a company looking to generate interest in data science at schools, emphasize what potential societal and social impacts students can have as data scientists at your company.

Big data is intuitive and clear

Data science, at least on the surface, can be pretty cloudy, and most people (and students) don’t even know what it means to be a data scientist. This was once a challenge for software engineering – but companies like Codecademy and CodeMonkey, along with initiatives like Scratch, have been part of a movement that democratized coding and made it simpler and more accessible, at least at the beginner level. An easy code editor in Codecademy has shown students (plenty of my friends use it) what coding is, how you generally do it, and why it’s exciting. Bottom line: if you’re a college student, chances are you know what programming is, and you might have even tried it a little bit.

That’s not true for data science. Students generally have heard of the term but don’t know what a data scientist does, or the tools he/she would use at the job. The bulk of basic data science work – numpy, matplotlib, and pandas, if you’re using Python – isn’t easy, but it’s often intuitive and powerful. Students need a clear walkthrough and framework for what data science is, how they do it, and what cool things they can find and create with it. Something interactive like Codecademy for a relevant data set, like world population or GDP, could help get students interested.

I think that these are the core issues, but they aren’t the only solutions offered. Amy Gershkoff believes that more formal education and frameworks are needed. But I’m taking a different route – I think it’s more about the big picture potential and practical hands-on experience. These two topics – the potential positive impact of data science and the cloudiness surrounding it – definitely overlap. But solving them could be the breakthrough needed to get students interested and majoring in the field. Let’s get to it.

Justin Gage is a student at NYU’s Stern School of Business, where he’s majoring in Data Science. He’s the National Director of Consulting for TAMID Group, a student organization on 30+ college campuses that develops student business skills through hands-on interaction with the Israeli economy. This summer, he’s working at Cornerstone Venture Partners, an early stage VC firm that invests in software companies, with sights on VC for the long run. He loves fashion, well designed stuff, and all things tech.

Personally, I’ve been guilty of this attitude in the past. In one of my first corporate jobs, I was thoroughly engaged – working long hours and finding ways to show my value. Then one day, my eagerness backfired. “If I promote you, I can’t possibly find someone to replace your current position and everything you do. You run a well-oiled machine. Why would I ruin that?” said one of my managers.

Needless to say, this conversation quickly deflated any sense of engagement for me. At the same time, I learned something very valuable: Disengagement is not a sign that employees don’t care. It’s a disconnect between executive leadership and their employees.

Disengagement is not about caring – it’s about connecting

Even though most employers provide the basic benefits and incentives employees want, there are some intangible incentives that employee value even more. According to the latest Workforce 2020 research from Oxford Economics, sponsored by SAP, employees indicated that they want to know that their work is meaningful and serves a significant purpose. They desire recognition for their performance and success, education opportunities to expand their skill sets, and career advancement.

Unfortunately, this is not happening in most companies. In fact, executives painted a different picture with their belief that bonuses, training, benefits, and personal recognition enhance engagement.

When you look at the gap between what executives think and what employees want, you can easily see the problem. More money and perks are not the answer to creating a more engaged workforce. Instead, executives have to reach the hearts of their employees to create a culture that meets their core needs.

Spiritual connection of serving a higher purpose at work and doing what they enjoy and/or do best

Whenever these needs are supported, employees are more likely to be engaged, loyal, and full of positive energy. More important, stress levels are lower. The more needs that are met, the more likely employees decide to stay and become an active contributor to the overall business’ success.

But first, you must create a workplace culture that addresses and nurtures these basic needs. Check out the infographic A Guide to Employee Engagement for a five-step approach to increasing employee engagement.

What do you think? How do you keep employee engagement alive in your company? Please share your story!

Recommended article: Chomsky: We Are All – Fill in the Blank.This entry passed through the Full-Text RSS service – if this is your content and you’re reading it on someone else’s site, please read the FAQ at fivefilters.org/content-only/faq.php#publishers.

As digital experiences have evolved, the tools of the trade have had to keep pace. High on the list are rich media — such as photos, audio and video — that enable viewers to get a quick introduction into an organization’s message or a company’s brand.

There are emerging new roles for rich media to support digital tasks. Ten years ago, who thought high-definition movies would become as ubiquitous as phone calls — and as easy to create as email messages? With the advent of rich media, it now seems second nature to add audio and video to round out our digital experiences.

Today, websites use rich media for e-commerce, online customer service, sales enablement, marketing and digital branding. But the ways companies use these tools to extend their reach is ever-more important to digital experiences. Images, audio and video are often the front door to the user’s interaction with a site, a company and a brand.

But challenges abound with these files. Video and audio need editing, and they sometimes need permissions to be requested and then made available for use on a webpage, and they can put pressure on network bandwidth. Finding the right tools to manage these assets and weave them into the total digital experience are still challenges that traditional tools haven’t satisfactorily accomplished. Traditional tools have often created static images or video that doesn’t account enough for the surrounding Web context they reside in. These elements also haven’t worked responsively on mobile devices or been able to display differently according to a site visitor’s profile and preferences.

The digital world is exponentially larger than it was 20 years ago, and DAM systems have not kept pace.

These traditional tools include digital asset management (DAM) systems, which have been around for almost 20 years. DAM software now includes myriad features, but they continue to support only a few targeted tasks. At their core, these systems remain powerful cataloging and publishing engines that primarily manage visual assets and associated metadata. They are designed to group like things together into common collections for production, distribution and broadcast. But they don’t necessarily support the flexible workflow that is often required of business operations in creating content, creating marketing messaging and so forth. Not surprisingly, the digital world is exponentially larger than it was 20 years ago, and DAM systems have not kept pace.

New trends in managing rich media

Historically, innovative approaches for enhancing tasks and processes within digital environments have been lacking. Lately, though, I am detecting the winds of change.

Two examples have contextualized rich media for inside sales productivity and interactive learning. What’s significant is how, with the right amount of management, rich media enlivens these tasks.

Sales productivity. When it comes to sales outreach, it is now easy to add emotional elements through personalized video messages. The mechanisms for VSNAP, a sales acceleration suite launched by the Boston-based startup, are simple. Inside salespeople record and send short video clips to prospects by emailing or tweeting clickable links.

VSNAP adds the human dimension to communications, enabling sales reps to convey a tone and establish trust through individual video messages. VSNAP also includes a dashboard to append results and integrates with Salesforce.com for systematic customer relationship management.

It’s the “modern version of the handwritten note that feels personal,” VSNAP CEO and co-founder Dave McLaughlin explained. “Sales is all about engaging buyers and relating to them in an emotional way.” According to McLaughlin, some VSNAP accounts have reported a 34% increase in their close rates, despite the noisy space of digitally driven selling.

Weaving experiences for interactive learning. When it comes to learning, rich media adds intuitive elements to the experience. The challenge is contextualizing the content to accommodate learners’ goals and intentions.

Mechon Hadar, a nonprofit Jewish learning institution, combines traditional study with leadership development and innovation. Last year, the organization redesigned its website to re-establish itself among its communities of learners and stakeholders. Working with See3 Communications as its digital agency and integration partner, Mechon Hadar has crafted contextualized learning experiences.

In addition to a modern look that mobilizes experiences among multiple devices, Mechon Hadar needed to catalog and offer up more than 10,000 pieces of content — from songs and wordless melodies, to weekly Torah discussions — to dozens of video lectures. This includes “the ability to instantly play any song or melody or video, or pull up any piece of content [among] thousands, and to download individual pieces and collections,” said Michael Hoffman of See3.

The organization developed a well-defined information architecture based on predefined user personas. Subject matter experts from Mechon Hadar collaborated with See3 designers to develop various taxonomies for weaving together content into different kinds of experiences. Mechon Hadar also uses Drupal, a Web content management (WCM) system. In addition to flexibility and scalability, Drupal supports responsive Web design to deliver content to multiple devices.

Drupal manages content in context, even when the content elements are rich media types. In addition to well-established topic categories about liturgy, philosophy, social issues and lifecycle events, the project team focused on indexing audio and video assets. Categories for music include tempo, meter and tone. This kind of categorization enables creative “mashups” of existing content.

“We want to make it easy for our students to remix melodies, hear tunes that work well together, and develop their own musical themes,” said Jason Rubenstein of Mechon Hadar. Learners can explore options interactively, and associate melodies with traditional texts to create their own interpretations.

Rich media, including audio and video, should be like any other content type and can be managed through a third-generation WCM platform. With deep linking, it’s now possible to get inside these files and get information about discrete portions — whether it’s a riff in a melody or a few seconds in a video.

Delivering task-oriented results

Our expectations about the role and impact of rich media are very different today from 2007 — before the iPhone launched an accelerating mobile revolution. We expect audio and video experiences to be integral to workday activities. We now face the challenge of how best to build and deliver the engaging solutions that can meet (or exceed) our expectations. Making rich media useful depends on contextualizing it and having the flexibility to deliver task-oriented results that add value to the total Web experience.

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